/*
 * Copyright (c) 2017 Uber Technologies, Inc. (hoodie-dev-group@uber.com)
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *          http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.uber.hoodie.table;

import com.uber.hoodie.common.model.HoodieRecord;
import com.uber.hoodie.common.model.HoodieRecordPayload;
import org.apache.spark.api.java.JavaRDD;

/**
 * Repartition input records into at least expected number of output spark partitions. It should
 * give below guarantees - Output spark partition will have records from only one hoodie partition.
 * - Average records per output spark partitions should be almost equal to (#inputRecords /
 * #outputSparkPartitions) to avoid possible skews.
 */
public interface UserDefinedBulkInsertPartitioner<T extends HoodieRecordPayload> {

  JavaRDD<HoodieRecord<T>> repartitionRecords(JavaRDD<HoodieRecord<T>> records,
      int outputSparkPartitions);
}
